Integrated disassembly and reprocessing lot-sizing for multi-level structured products in remanufacturing systems

2021 ◽  
pp. 1-18
Author(s):  
Hyoung-Ho Doh ◽  
Dong-Ho Lee
2000 ◽  
Vol 51 (11) ◽  
pp. 1309 ◽  
Author(s):  
Y.-F. Hung ◽  
K.-L. Chien

2019 ◽  
Vol 9 (7) ◽  
pp. 1464 ◽  
Author(s):  
Alfonso Romero-Conrado ◽  
Jairo Coronado-Hernandez ◽  
Gregorio Rius-Sorolla ◽  
José García-Sabater

The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.


2021 ◽  
Vol 9 (1) ◽  
pp. 127-133
Author(s):  
V. V. D. Sahithi, M. Srinivasa Rao, C. S. P. Rao

In this competitive and constantly changing world, meeting the customer requirements within less time by providing less cost is extremely tricky task. This is only possible by optimizing all the different parameters in its life cycle. Here Optimizing the inventory plays a major role.Maintaining the exact amount of inventory, at proper place, in appropriate level is a challenging task for production managers. When we work on Multi level environments this problem becomes even more complex.So, to optimize this kind of problems we applied binary form of Flower Pollination algorithm to solve this complex problem. we solved different inventory lot sizing problems with this FP algorithm and compared the results with genetic algorithm and other algorithms. In all the scenarios our simulation results shown that FP algorithm is better than other algorithms.                       


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